Among technologies for acquiring three-dimensional position information on an object to be measured is one that measures a range image by a pattern projection method.
According to the pattern projection method, a projection apparatus initially projects a two-dimensional pattern onto a space to be measured. An image capturing unit (camera) then captures an image of the space to be measured, and a pattern is detected from the captured image. A distance to the object to be measured is measured by triangulation using the detected pattern.
Distance values to be output are typically expressed as pixel values of a captured image. Such an image whose pixels indicate a distance is referred to as a range image.
A projection pattern used to measure a range image includes figures that are used to calculate a distance by triangulation (hereinafter, referred to as distance measurement figures) and figures that include an identifier embedded to distinguish a distance measurement figure from others (hereinafter, referred to identification figures). Examples of the distance measurement figures include a straight line. Examples of the identifiers include a geometric pattern and a color combination.
To improve the measurement density of a range image in a space to be measured, it is desirable to increase the density of distance measurement figures in a projection pattern. In the meantime, such distance measurement figures need to be distinguishable from each other for the sake of triangulation. Identification figures that allow unique identification in the entire pattern therefore need to be embedded in the distance measurement figures for the purposes of distance measurement.
Another approach is predicated on that an object to be measured lying in a space to be measured is a continuous smooth surface. According to the method, distance measurement figures are identified based on the continuity of the distance measurement figures without using identification figures.
Thomas P. Knonckx and Luc Van Gool, “Real-Time Range Acquisition by Adaptive Structured Light,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 28, No. 3, pp. 432-445, (2006), discusses a method that includes continuously measuring a range image to detect a change of identification figures based on the fact that distance measurement figures do not vary greatly between consecutive frames, and changing a projection pattern. In the above document, the distance measurement figures include parallel lines, whereas the identification figures include oblique lines in different colors. The projection pattern includes figures to be a code, which are arranged at the intersections of distance measurement figures and identification figures.
The presence or absence of the arranged codes is interpreted as a code string and used as the number of an adjoining distance measurement figure. A projection pattern is changed according to detection errors of identification figures and distance measurement figures between consecutive frames. If detection errors are large, the intervals of distance measurement figures are increased and repetitions of identification figures are reduced. Since the distances between adjoining figures are widened, the number of distance measurement locations decreases but with a reduction in the possibility of erroneous detection. The total amount of distance points varies less between frames.
A range imaging acquisition method using a single projection pattern can acquire a range image in a shorter time than by techniques that need a plurality of projection patterns. Such a range imaging acquisition method is thus used in situations where a high speed response is required. According to the technology discussed in the foregoing document, the detection accuracy of distance measurement figures and identification figures depends on a surface shape to be measured.
The higher the density of distance measurement figures becomes, the more likely erroneous detection occurs between adjoining distance measurement figures. Identification figures also need to have a greater number of elements. Each individual figure element becomes greater, which makes a threshold for identification determination difficult to set.
The foregoing document discusses a method for adjusting such projection patterns. The method includes adjusting repeat intervals of the distance measurement figures and the identification figures according to variations of detection errors during continuous range imaging.
Since the method uses an erroneous detection ratio of a pattern projected onto a target object that varies gently between consecutive frames, the application of the method to highly variable environment or to a case of capturing only a single range image has been difficult. In other words, there has been no mechanism for a single projection pattern that allows effective acquisition of a range image.